Adaptive removal of resonance-induced noise

ABSTRACT

Noise is removed from the digitized output of a sensor, subject to undesired resonance, even when the resonant frequency is unknown or drifts, with sufficiently low phase delay for the sensor to be used in closed-loop control. A very narrow notch filter which removes the resonance-induced noise is recursive (IIR) and therefore has a low phase delay. However, the apparatus which determines the center frequency of the notch filter is non-recursive, and therefore stable. It includes a tunable FIR filter which tracks the same resonance that we wish the IIR filter to remove. Tuning the FIR filter to minimize the output of the FIR filter therefore tunes the notch frequency to align with the resonant frequency. The tuning parameter which adaptively produces this result is suitably scaled and biased, and is applied to the IIR filter.

BACKGROUND OF THE INVENTION

This invention relates to removing noise from the digitized output of asensor, the sensor being subject to undesired (although perhapsnecessary) internal or external resonance. It further relates to suchremoval when the resonant frequency is unknown or drifts.

A popular form of angular rate sensor includes a piezoelectric tuningfork. When the fork is rotated, coriolis forces distort the forkproportionally to the magnitude of the rotation. Effects of resonance ofthe tuning fork, however, must be removed from the output signal fromthe fork. This is possible, with a notch filter, if the effect is at afrequency removed from the frequency of interest by an order ofmagnitude. This is often the case.

In the foregoing example, the resonance is internal to the sensor. It isequally desirable to remove resonance-induced noise from the output of asensor even when the resonance is external to the sensor. This wouldoccur, for example, in electrical equipment powered by an unstablesupply. 60-cycle hum from commercially supplied electricity is easilynotched out, but the unstable output of an emergency generator can makeits way into a signal to be measured, and is much more difficult toremove. Again, the resonant frequency (and its effect) must be at afrequency somewhat removed from the frequency of interest.

We return to the angular rate sensor with an underlying operatingfrequency which must be removed from its output signal. This removal isrelatively straightforward with a (digital) stagger-tuned notch filterwhen the frequency range is somewhat known. Stagger-tuned notch filters,however, introduce considerable phase lag.

When the frequency is grossly unknown, unstable, or both, stagger-tunedfilters introduce so much phase lag--even at frequencies at somedistance below the notch frequency--as to make them unsuitable for animportant application: closed-loop control. The solution is to use avery narrow adaptive notch filter, the very narrowness of which greatlyreduces phase lag. However, a very narrow notch filter must be aninfinite impulse response (IIR) filter; it must be recursive. This inturn makes the adaptive tracking of the notch frequency of the filterunstable: there are many relative minima on the performance-criterionsurface. This in turn makes it unsuitable for closed-loop control.

What is needed is an IIR filter to notch out the objectionable resonancewith the stable adaptive properties of a non-recursive, finite impulseresponse (FIR) filter. This problem seems insoluble.

SUMMARY OF THE INVENTION

Applicants have solved the problem by noting a hidden distinction in thestatement of the problem. The very narrow notch filter which removes theresonance-induced noise must have a low phase delay and therefore mustbe recursive. However, the apparatus which determines the centerfrequency of the notch filter may be non-recursive, and thereforestable.

This center-frequency apparatus includes a tunable FIR filter whichtracks the same resonance that we wish the IIR filter to remove; thatis, the numerator of its transfer function has zeroes at the samevalues. Most of the energy of the input signal is in the resonant noise,not the measurement of the parameter. Tuning the FIR filter to minimizethe output of the FIR filter therefore tunes the notch frequency toalign with the resonant frequency. The tuning parameter which adaptivelyproduces this result is suitably scaled and biased, and is applied tothe IIR filter, the numerator of whose transfer function is preciselythe same as that of the transfer function of the FIR filter. Because thetuning parameter was adaptively generated in an FIR filter, it isstable. Because it is applied to an IIR filter to filter the raw outputof the sensor, the raw output is filtered without significant phasedelay.

The foregoing assumes that the resonant frequency, to be notched out,drifts relatively slowly. This is usually the case. If the resonantfrequency drifts rapidly, then the phase delay inherent in the emulatingFIR filter will not allow the tuning parameter to drift quickly enoughto follow it. If this happens, unacceptably large amounts of resonantfrequency noise will be passed by the IIR filter. The present inventionshould not be used in such situations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic of an angular-rate measurement system in which theoutput of a quartz rate sensor (QRS) is sampled, digitally demodulated,and applied to the present invention: a recursive tunable notch filterand a tuning parameter estimator.

FIG. 2 shows the details of the tuning parameter estimator of FIG. 1: abandpass filter, an optional automatic gain control (AGC), and a betaestimator.

FIG. 3 shows the details of the beta estimator of FIG. 2: a tunablefinite impulse response (FIR) notch filter, a cross-correlator, and anoutput circuit.

FIG. 4 is a digital block diagram of the tunable FIR notch filter ofFIG. 3.

FIG. 5 is a digital block diagram of the cross-correlator and the outputcircuit of FIG. 3.

FIG. 6 is a digital block diagram of the AGC of FIG. 2, including avariance estimator.

FIG. 7 shows the details of a filter topology used extensively in thisinvention, a Gray-Markel (GM) second-order two-multiplier recursiveallpass lattice filter.

FIG. 8 shows the recursive tunable notch filter of FIG. 1, including aGM.

FIG. 9 shows the bandpass filter of FIG. 2, made of GM sections.

FIGS. 10, 11, and 12 show three different embodiments of the varianceestimator of FIG. 6.

DETAILED DESCRIPTION OF THE DRAWINGS Overall View of the Invention

FIG. 1 is a schematic of an angular-rate measurement system in which theoutput of a quartz rate sensor (QRS) 3 is sampled 5, 7, digitallydemodulated 9, and applied to the present invention 17: a recursivetunable notch filter 15 and a tuning parameter estimator 19. Arotational body rate 1 is detected by a high-Q quartz rate sensor (QRS)3. QRS 3 produces a double-sideband suppressed-carrier rate outputsignal 5, which contains a large component of resonance-induced noise.QRS 3 also produces a reference signal 7 suitable for demodulating therate output signal 5. Both signals 5, 7 are sampled at a rate T (or,equivalently, a sampling frequency f(s)) and digitally demodulated indemodulator 9. Demodulator 9 produces a QRS demodulated output signal11.

Co-applicant White has done extensive research on the QRS, and isapplicant or co-applicant of the following applications and patents, thedisclosures of which are hereby incorporated herein by reference:

Applications:

    ______________________________________    Serial    Number  Title                   File Date    ______________________________________    08/120,871            Amplitude Detection and Automatic Gain                                    09/07/93            Control of a Sparsely Sampled Sinusoid by            Computation Including a Hilbert Transform    08/634,003            Sawtooth Phase Filter   04/15/96    08/636,088            Measuring Amplitude of Sparsely Sampled                                    04/22/96            Sinusoidal Signal    08/676,653            Decimating IIR Filter   07/08/96    08/683,643            Adaptive Phase-Shift Adjuster for Resonator                                    07/15/96    ______________________________________    Patents:    Patent                          Issue    Number  Title                   Date    ______________________________________    5,179,380            One-Bit Sigma-Delta Modulator with Improved                                    01/12/93            Signal Stability    5,339,263            Decimator/Interpolator Filter for ADC                                    08/16/94            and DAC    5,361,036            Complex Digital Demodulator Employing                                    11/01/94            Chebychev-Approximation Derived Synthetic-            Sinusoid Generator    5,400,269            Closed-Loop Baseband Controller for a                                    03/21/95            Rebalance Loop of a Quartz Angular-Rate            Sensor    5,444,639            Angular-Rate-Sensing System and Method with                                    08/22/95            Digital Synthesizer and Variable-Frequency            Oscillator    5,444,641            Admittance-Parameter Estimator for a                                    08/22/95            Piezoelectric Resonator in an Oscillator Circuit    5,459,432            Use of a Chopper and a Sigma-Delta Modulator                                    10/17/95            for Downconverting and Digitizing an Analog            Signal Including Information Modulated by a            Carrier    5,463,575            Reduced Quantization Noise from a                                    10/31/95            Single-Precision Multiplier    5,471,396            Estimator of Amplitude and Frequency of a                                    11/28/95            Noisy Biased Sinusoid from a Short Burst            of Samples    5,487,015            Self-Oscillating Driver circuit for a Quartz                                    01/23/96            Resonator of an Angular-Rate Sensor    5,491,725            A Tracking Filter and Quadrature Phase-                                    02/13/96            Reference Generator    5,550,866            A Demodulator/Reference Generator Based on                                    08/27/96            Two Cascaded Hilbert Transformers    5,566,093            Sensor with Resonator, Digital Filter,                                    10/15/96            and Display    5,576,976            Amplitude Detection and Automatic Gain Con-                                    11/19/96            trol of a Sparsely Sampled Sinusoid by            Adjustment of a Notch Filter    5,577,073            A Frequency and Phase-Locked Two-Phase                                    11/19/96            Digital Synthesizer    ______________________________________

All other references cited herein are also incorporated herein byreference.

Output signal 11 is often rendered useless because of a large resonancenoise component included from double-sideband suppressed-carrier rateoutput signal 5. The frequency of the resonance noise drifts. Outputsignal 11 is therefore applied to a tunable notch filter 15, which canremove the resonance if it is driven by a tuning-parameter value whichis fixed or suitably programmed. The notch filter 15 is recursive, toprovide low phase shift. The present invention 17 combines the recursivetunable notch filter 15 with an automatic tuning-parameter estimator 19.The output 21 of the estimator 19 is an estimate of the tuning-parameterbeta needed to correctly tune the recursive tunable notch filter 15

Tuning Parameter Estimator

FIG. 2 shows the details of the tuning parameter estimator of FIG. 1: abandpass filter 23, an optional automatic gain control (AGC) 27, and abeta estimator 31. The input 11 to the tuning-parameter estimator 19 isthe QRS demodulated output signal. This signal 11 contains theobjectionable resonance noise as well as the information signal and acomplex noise structure. The frequency of the resonance noise is knownto lie between a maximum frequency f(max) and a minimum frequencyf(min), and the frequency of the information signal is known to lieoutside these limits. Bandpass filter 23 is therefore constructed topass signals within this frequency band, and to reject all others. Thebandpass filter output signal 25 is therefore dominated by the resonancenoise. Automatic Gain Control (AGC) 27 adjusts the amplitude of theresonance noise so that its output 29 lies in the most effectiveamplitude range for achieving a fast and accurate response from the betaestimator 31. The output 21 of the beta estimator 31 is the previouslydiscussed output of the tuning parameter estimator 19. This is thesought-after tuning parameter value for the recursive tunable notchfilter 15.

Beta Estimator

FIG. 3 shows the details of the beta estimator 31 of FIG. 2: a tunablefinite impulse response (FIR) notch filter 45, a cross-correlator 39,and an output circuit 43. The beta-estimator input signal 29 drives thetunable FIR notch filter 45. The filter 45 has two outputs: delayedoutput 33 and notch filter output 35. If the filter 45 is properly tunedby the feedback tuning signal 37 from the cross-correlator 39, then theenergy of the notch-filter output 35 should be very small. The output 41from the cross-correlator 39 is the same as the feedback tuning signal37, but instead drives the output circuit 43. Output circuit 43 in turnbiases and scales the cross-correlator output signal 41 to form thetuning-parameter value 21.

Tunable FIR Notch Filter

FIG. 4 is a digital block diagram of the tunable FIR non-recursivedigital notch filter 45 of FIG. 3. It includes two scaling elements 53and 55, the coefficients for which are based on an FIR filter operatingrange from f(min) to f(max). This is preferably also the passband ofpassband filter 23. It is further preferred that this range closelyapproximate the frequency range of the resonance noise. If this is done,then the full dynamic range of the components can be exploited, which isespecially desirable when fixed-point arithmetic components are used (asis preferred).

All that is necessary, however, is that the FIR filter operating rangecompletely include the noise range, and that the passband filter'spassband also completely include the noise range. If additionalfrequencies, overlapping or not, are included in FIR filter operatingrange, or the passband, then the apparatus will still function, but notas accurately (especially with fixed-point devices).

What is vital is that coefficients of the output circuit 43 (see alsoFIG. 5) be based on the operating range of FIR filter 45, and not thepassband of passband filter 23. The FIR notch center frequency of FIRfilter 45 is based on its operating range, determined by thecoefficients of multipliers 53, 55 and the value of control signal 37from cross-correlator 39, and not on the range of signals 29 which areapplied to it, whether directly or through a passband filter.

AGC 27 is optional since its presence does not affect the notchfrequency of the FIR filter 45. Instead, its presence allows thefixed-point arithmetic devices to exploit their full range, withouteither overflow or underflow. If floating-point devices are use, AGC 27becomes less necessary.

In FIG. 4, the beta estimator input signal 29, x(n), feeds first delayelement 47 to produce a singly-delayed signal 33, x(n-1), which in turnfeeds second delay element 49 to produce a doubly-delayed signal 51,x(n-2).

Adder/subtracter 59 forms the sum

    x(n)+x(n-2).

First scaler 53 produces the product

    x(n-1)*2*BETA(0),

and adder/subtracter 59 also subtracts this product from the previouslymentioned sum

    x(n)+x(n-2),

the difference being the output 61 of adder/subtracter 59. Let

    BETA(0)=(1/2)* BETA(max)+BETA(min)!,

where

    BETA(max)=cos(2*PI*f(min)*T),

and

    BETA(min)=cos(2*PI*f(max)*T).

Thus, BETA(0) is a mid-point estimate of the BETA which is to be appliedto IIR filter 15 to set the notch frequency of IIR filter 15. Firstscaler 53 thus scales x(n-1) by a first coefficient equal toapproximately twice the BETA coefficient of the IIR filter 15. Output 61is thus seen to be the output of an FIR notch filter when tuned toBETA(0).

Actual tuning of the tunable notch filter 45 is accomplished bymanipulating DELTA BETA, where

    DELTA BETA=1/2* BETA(max)-BETA(min)!.

DELTA BETA is thus seen as the half-width of the range of the BETAs tobe applied to the IIR 15. DELTA BETA is exploited by a control signalu(n), 37, which is received from the cross-correlator 39. Control signal37 is the cross-correlation coefficient between singly-delayed inputsignal 33 and FIR output signal 35, and is constructed to fall between-1/2 and +1/2. It represents the true coefficient plus an error measure,which is adaptively driven to zero. Singly-delayed signal x(n-1), 33,and control signal u(n), 37, are multiplied together in first multiplier57, the product of which is multiplied by

    4*(DELTA BETA)

in second scaler 55. Thus, u(n)*x(n-1) is multiplied by a secondcoefficient equal to twice the expected range of the BETA coefficient ofthe IIR 15. The product 63,

    u(n)*x(n-1)*4*(DELTA BETA),

of multiplier 55 is subtracted from output 61 in subtracter 65, therebyproducing output 35, y(n), the FIR output signal. Thus,

    y(n)=x(n)-2*BETA*x(n-1)+x(n),

where

    BETA=BETA(0)+2*(DELTA BETA)*u(n).

Note that,

    when u(n)=1/2, BETA=BETA(max),

and that,

    when u(n)=-1/2, BETA=BETA(min).

Control signal u(n), 37, is manipulated through DELTA BETA, rather thanthrough BETA itself, because DELTA BETA has such a smaller magnitude.This topology greatly reduces the effects of round-off error from thedigitizing process. These effects are especially pronounced whenfixed-point arithmetic devices are used, as is preferred, since they aresmaller, cheaper, lighter, and more power miserly. Even whenfloating-point devices are used, however, this topology reduces theoutput errors which occur when enough round off errors alignsimultaneously.

The transfer function of the FIR filter 45 is readily determined.

    For -1/2<u(n)<1/2,

    y(n)=x(n)+x(n-2)-x(n-1) 2*BETA(0)+4*(DELTA BETA)*u(n)!,

or

    y(n)=x(n)+x(n-2)-x(n-1)*2* BETA(0)+2*(DELTA BETA)*u(n)!,

or

    y(n)=x(n)+x(n-2)-x(n-1)*2* BETA!.

Thus,

    Y(z)=X(z)+z (-2)X(z)-2z (-1)X(z)BETA,

or

    Y(z)/X(z)=1-2z (-1)BETA+z (-2).

This is identical to the numerator of the transfer function of the IIRfilter 15 of FIG. 8 (see also FIG. 1), which is what we want.

Cross-Correlator

FIG. 5 is a digital block diagram of the cross-correlator 39 and theoutput circuit of FIG. 3. Cross-correlator 39 exists to produce controlsignal u(n), 37, in such a fashion that, when applied to the tunable FIRnotch filter 45, the filter's output y(n), 35, will have minimum power.The control law for control signal u(n), 37, is obtained by invoking thesteep descent law. Under the steep descent law, the rate of adjustmentof u(n) is made proportional to the partial derivative of the unbiasedestimate of the output power E(y(n)squared) with respect to u(n). Thisis the well-known LMS criterion, popularized by Widrow. See B. Widrowand J. M. McCool, "A comparison of adaptive algorithms based on themethods of steepest descent and random search," IEEE Trans. AntennasPropag., vol. AP-24, no. 5, pp. 615-637, September 1976; B. Widrow, J.M. McCool, M. G. Larimore, and C. R. Johnson, Jr., "Stationary andnonstationary learning characteristics of the LMS adaptive filter,"Proc. IEEE, vol. 64, no. 8, pp. 1151-1162, August 1976; B. Widrow etal., "Adaptive noise canceling; principles and applications," Proc.IEEE, vol. 63, no. 12, pp. 1692-1716, December 1975; B. Widrow,"Adaptive Filters," in Aspects of Network and System Theory, R. E.Kalmas and N. De Claris (Eds.), New York: Holt, Rinehart and Winston,1970, pp. 563-587; and B. Widrow and E. Walach, "On the statisticalefficiency of the LMS algorithm with nonstationary inputs," IEEE Trans.Information Theory--Special Issue on Adaptive Filtering, vol. 30, no. 2,part 1, pp. 211-221, March 1984.

Cross-correlator 39 is mechanized to invoke this law. Second multiplier67 forms the product of signals 33 and 35, which is a scaled gradient ofthe power of the FIR output signal 35 with respect to the tuning controlsignal 37; that is, it is a partial derivative of an unbiased estimateof the power of the FIR output signal 35. The resulting product issummed with tuning control signal 37 in second adder 69. Limiter 71limits the output of adder 69 to lie between +1/2 full scale and -1/2full scale. The output of limiter 71, when delayed by third delayelement 73, forms the tuning control signal u(n), 37, for the tunableFIR notch filter 45.

Output Circuit

Control signal u(n), 37, is fed back to the tunable FIR notch filter 45.It is also fed forward, as cross-correlator output 41, to drive outputcircuit 43. Output circuit 43 scales the cross-correlator output 41 by2*(DELTA BETA) in third scaler 75; that is, it multiplies the controlsignal 37 by a third coefficient, 2*(DELTA BETA), equal to half thesecond coefficient, 4*(DELTA BETA). See above, "Tunable FIR NotchFilter", FIG. 4, second scaler 55. It then biases the result by BETA(0)in third adder 77 to produce BETA, the tuning-parameter value 21; thatis, it adds the result and a fourth coefficient, BETA(0), which is equalto half the first coefficient, 2*BETA(0). See above, "Tunable FIR NotchFilter", FIG. 4, first scaler 57.

Output circuit 43 thus generates BETA, a function of the control signal37, which is a linear bias and scale function. This circuit is greatlypreferred for its simplicity, although non-linear functions could beused. In any event, the function is selected such that the FIR notchcenter frequency is the same as the IIR notch center frequency when thecontrol signal 37 is applied to the FIR filter 45 and the function ofthe control signal 37 is applied to the IIR filter 15.

Output circuit 43 is necessary since the range of the cross-correlatoroutput 37, 41 was determined to allow robust operation of thecross-correlator with finite word-length arithmetic. Signal 41 rangesfrom -1/2 to +1/2, while the tuning parameter value ranges fromBETA(0)-(DELTA BETA) to BETA(0)+(DELTA BETA). The output circuit 43provides the necessary scaling

    2*(DELTA BETA)=1

and

    BETA(0)=0.

This would occur if

    BETA(max)=1/2,

that is,

    f(min)=f(s)/6=1/(6*T),

and

    BETA(min)=-1/2,

that is,

    f(max)=f(s)/3=1/(3*T).

This would also eliminate the signal path through scaler 53, and wouldreplace scaler 55 with a doubler. This simplification is not preferred,since it imposes severe restrictions on the range of resonancefrequencies which can be notched out. The identity function thusproduced, while undesirable, is the simplest possible linear bias andscale function.

Automatic Gain Control

FIG. 6 is a digital block diagram of the AGC of FIG. 2, including avariance estimator 83. Multiplier 79 scales the bandpass filter outputsignal 25 by AGC output signal 81 to produce the beta-estimator inputsignal 29. Variance estimator 83 provides an estimate 85 of the varianceof the bandpass filter output signal 25. This variance estimate 85 isput to two uses.

In the first use, multiplier 89 squares the AGC output signal 81, andmultiplier 87 multiplies this square by the variance estimate 85. Theresulting product 91 is an estimate of the variance of thebeta-estimator input signal 29. Adder/subtracter 95 takes the product 91and subtracts it from the sum of the AGC reference value 93 and the AGCoutput signal 81. Limiter 97 limits the result to lie between 0 and 1,and delay element 99 delays the limited result to for the AGC outputsignal 81.

The AGC reference value 93 is the target value of the variance of thebeta-estimator input signal 29. Since estimated variance signal 91estimates the variance of input signal 29, the difference formed in theadder/subtracter 95 is an estimate of the error in setting the inputsignal 29 to the proper power level. Such an error would be due to anerror in setting the AGC output signal 81. The AGC output signal 81 istherefore driven to minimize the variance error. The AGC reference value93 is predetermined from the nature of the sensor output to be filtered,the size of the registers of the arithmetic units which mechanize thefilter, and the like, as determined by the ordinarily skilled worker.

In the second use, a subtracter 103 subtracts the variance estimate 85from a threshold reference 101. If the difference is positive, that is,if the variance estimate 85 is less than the threshold reference 101,then a logic unit 105 produces a control output 107 of "TRUE". This, inturn, inhibits further operation of (in FIG. 5) the adder 69, and (inFIG. 6) the adder/subtracter 95. The former inhibition freezes thetuning-control signal 37, and thus freezes the zeroes of the tunable FIRnotch filter 45 (FIG. 3). The latter inhibition freezes the AGC outputsignal 81, and allows the bandpass filter 23 to drive the beta estimator31 as though the AGC 27 were a simple scaler.

When the variance estimate 85 is this low, this indicates that the errorbetween the notch center frequency and the resonance noise centerfrequency is so small that the IIR 15 should be kept as it is. It canalso indicate that the signal levels are so low that no meaningfuladaptation is possible.

Gray-Markel Filter

FIG. 7 shows the details of a filter topology used extensively in thisinvention, a Gray-Markel (GM) second-order two-multiplier allpassrecursive lattice filter 109. This filter is entirely conventional, andits transfer function is stated in FIG. 7. It will accordingly not befurther discussed. See Gray, et al., "Digital lattice and ladder filtersynthesis," IEEE Trans. on Audio and Electroacoustics, vol. AU-21, no.6, pp. 491-500, December 1973.

Recursive (IIR) Tunable Notch Filter

FIG. 8 shows the recursive (IIR) tunable digital notch filter 15 of FIG.1, including a GM 109. In the GM 109, the ALPHA coefficient is fixed atslightly below 1, and the BETA coefficient is the tuning parameter value21 from FIG. 5. IIR filter 15 has an IIR notch center frequency which istunable in response to BETA. A third multiplier 113 prevents overflow byone-half scaling the input signal 11 to an intermediate signal 111,which is applied both to the GM 109 and to a first adder 115. The outputof the GM 109 is also applied to the adder 115, the output 13 of whichis the output signal of the IIR 15. The transfer function of the IIR 15is stated in FIG. 8, and represents a very deep and narrow notch filterwith a very low phase delay at lower frequencies.

Bandpass Filter

FIG. 9 shows the bandpass filter of FIG. 2, made of cascaded GM sections119, 123, and 127. The sections 119, 123, and 127 are separated bysubtracters 121,125, and 129, each of which subtracts a GM filter outputfrom its input. A one-half scaler 117 receives the input signal 11, andparallels the one-half scaler 113 of FIG. 8, and likewise preventsoverflow. An output multiplier 131 restores the gain in the center ofthe passband to unity. The multiplier 113 may be omitted if, as ispreferred, the AGC 27 (see FIG. 2) provides full dynamic range to thebeta estimator 31. The transfer function, and suitable values for theALPHA and BETA of each section, are shown in FIG. 9. This is thepreferred form of bandpass filter, but any form of bandpass filter maybe used which is wide enough to admit substantially all of the noise tobe notched out, yet narrow enough to exclude the information signal.

Variance Estimators

FIGS. 10, 11, and 12 show three different embodiments of the varianceestimator of FIG. 6. Others will occur to those having skill in the art.

FIG. 10 shows a narrowband variance estimator. Input 25 drives a pair ofcascaded delay elements 133, 137. Subtracter 139 subtracts the output ofcascaded delay elements 133, 137 from the input 25. Scaler 141multiplies the output of subtracter 139 by

    K(0)=(1/4)*(csc 2*PI*F(min)*T!+csc 2*PI*F(max)*T!),

and produces output 143. Elements 133, 137, 139, and 141 comprise thequadrature-phase channel of a narrowband Hilbert transformer, whichtransforms input signal 25 to Q (imaginary) output signal 143. Thein-phase I (real) output signal is taken from center tap 135 betweendelay elements 133 and 137. Multiplier 145 squares the Q signal 143, andmultiplier 147 squares the I signal from tap 135. Adder 149 adds thesesquares. This approach works well if there is little noise in thefrequency region of the resonance; that is, when the resonance noise isa relatively clean sinusoid.

FIG. 11 shows a lowpass variance estimator that works very robustly, butslowly, in the presence of broadband noise. Slowness of operation isacceptable if the center frequency of the resonance noise driftsrelatively slowly, which is often the case. Multiplier 151 squares theinput signal 25, and multiplier 153 scales the square by one halfagainst overflow. The output 155 drives a lowpass filter comprising aMitra-Hirano type 1A(t) allpass filter and an adder 169, which addstogether the input 155 and output 157 of the Mitra-Hirano filter. See S.K. Mitra and K. Hirano, "Digital all-pass networks," IEEE Trans.Circuits and Systems, vol. CAS-21, no. 5, pp. 688-700, September 1974.

The input 155 of the Mitra-Hirano filter drives a delay element 159 andthe minus input of a subtracter 165. The output of subtracter 165 isscaled by a factor slightly less than one (0.9 is preferred) inmultiplier 161. An adder 167 adds the outputs of delay element 159 andmultiplier 161, and applies the sum 157 to delay element 163, which inturn drives the plus input of subtracter 165. The sum 157 is the outputof the Mitra-Hirano filter.

FIG. 12 shows an alternate narrowband variance estimator, tolerant ofmore noise near the resonance that the narrowband variance estimator ofFIG. 10. Multiplier 171 squares the input 25, and multiplier 173 scalesthe square by a factor of

    (1+ALPHA)/2,

where ALPHA is the ALPHA of the follow-on GM filter, that is,

    ALPHA=(1-tan PI*f(nw)*T!)/(1+tan PI*f(nw)*T!),

where f(nw) is the -6 dB notch width, in Hz. The result is applied tothe GM, but with the output taken just before the GM's ALPHA multiplier.The BETA of the GM filter is tuned to twice the center frequency of thebandpass filter 23, so, to a first approximation,

    BETA=cos(2*PI* f(max)+f(min)!*T).

Alternatively, we could follow squaring multiplier 171 with the notchfilter of FIG. 8, again with BETA tuned to twice the center frequency ofthe bandpass filter 23.

Scope of the Invention

Several specific embodiments of the present invention have beendisclosed herein, but the true spirit and scope of the present inventionare not limited thereto. Such limitations are imposed only by theappended claims and their equivalents.

What is claimed is:
 1. An article of manufacture comprising:(a) arecursive digital notch (IIR) filter constructed to receive an inputsignal and to produce an output signal, the IIR notch center frequencybeing tunable in response to a function of the control signal recitedbelow; (b) a bandpass filter constructed to receive the input signal andto produce a bandpass output signal; (c) a non-recursive digital notch(FIR) filter constructed to receive the bandpass output signal and toproduce an FIR output signal, the FIR notch center frequency beingtunable in response to the control signal recited below, and being thesame as the IIR notch center frequency when the control signal isapplied to the FIR filter and the function of the control signal isapplied to the IIR filter; and (d) a control signal generatorconstructed to generate a control signal which adaptively minimizes theFIR output signal.
 2. The article of claim 1, wherein the control signalgenerator is constructed to adjust the control signal at a rate which isproportional to the partial derivative of an unbiased estimate of thepower of the FIR output signal with respect to the control signal. 3.The article of claim 1, wherein the function is a linear bias and scalefunction with a bias other than zero or a scaling factor other than one.4. An article of manufacture comprising:(a) a recursive digital notch(IIR) filter constructed to receive an input signal and to produce anoutput signal, the IIR notch center frequency being tunable in responseto a function of the control signal recited below; (b) a bandpass filterconstructed to receive the input signal and to produce a bandpass outputsignal; (c) a non-recursive digital notch (FIR) filter constructed toreceive the bandpass output signal and to produce an FIR output signal,the FIR notch center frequency being tunable in response to the controlsignal recited below, and being the same as the IIR notch centerfrequency when the control signal is applied to the FIR filter and thefunction of the control signal is applied to the IIR filter; and (d) acontrol signal generator constructed to generate a control signal whichadaptively minimizes the FIR output signal; wherein the IIR filtercomprises a Gray-Markel filter having a beta coefficient, preceded by aone-half scaler and followed by a first adder connected to add togetheran input and an output of the Gray-Markel filter, and wherein the FIRfilter comprises: (e) a first delay element connected to receive aninput signal to the FIR filter and connected to produce a singly-delayedinput signal; (f) a second delay element connected to receive thesingly-delayed input signal and to produce a doubly-delayed signal; (g)a first scaler connected to receive the singly-delayed input signal andto multiply it by a first coefficient equal to approximately twice thebeta coefficient of the IIR filter; (h) an adder/subtracter connected toadd together the input and doubly-delayed signals and to subtract anoutput of the first scaler; (i) a first multiplier connected to multiplythe singly-delayed input signal and the control signal; (j) a secondscaler connected to receive an output of the multiplier and to multiplyit by a second coefficient equal to twice an expected range of the betacoefficient of the IIR; and (k) a subtracter connected to subtract anoutput of the second scaler from an output of the adder/subtracter, andto produce the FIR output signal.
 5. The article of claim 4, wherein thecontrol signal generator comprises:(a) a second multiplier connected tomultiply together the singly-delayed signal and the FIR output signal;(b) a second adder connected to add together a output of the multiplierand the control signal; (c) a limiter connected to limit an output ofthe second adder; (d) a third delay element connected to delay a outputof the limiter, and connected to produce the control signal.
 6. Thearticle of claim 4, wherein the control signal generator includes across-correlator connected to cross-correlate a delayed version of theinput signal and the FIR output signal and to thereby adjust the controlsignal at a rate which is proportional to the partial derivative of anunbiased estimate of the power of the non-recursive notched signal withrespect to the control signal, thereby adaptively minimizing the FIRoutput signal.
 7. The article of claim 6, further comprising an outputcircuit comprising:(a) a third scaler connected to multiply the controlsignal by a third coefficient equal to half the second coefficient; (b)a third adder connected to add together an output of the third scalerand a fourth coefficient equal to half the first coefficient, andconnected to produce the function of the control signal.